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Artificial Intelligence and Machine Learning Fundamentals
Duration
3
Course
LO-035439
Available Formats
Classroom Training, Online Training
Course Description
Overview
Artificial Intelligence and Machine Learning Fundamentals teaches you machine learning and neural networks from the ground up using real-world examples. After you complete this course, you will be excited to revamp your current projects or build new intelligent networks.Machine learning and neural networks are pillars on which you can build intelligent applications. Artificial Intelligence and Machine Learning Fundamentals begins by introducing you to Pythonand discussing AI search algorithms. You will cover in-depth mathematical topics, such as regression and classification, illustrated by Python examples.
As you make your way through the course, you will progress to advanced AI techniques and concepts, and work on real-life datasets to form decision trees and clusters. You will be introduced to neural networks, a powerful tool based on Moore's law.
By the end of this course, you will be confident when it comes to building your own AI applications with your newly acquired skills!
This course takes a hands-on approach to implementing different AI techniques and algorithms using Python. This course does not delve into the basics of Python. It is recommended to know basic Python programming and high-school mathematics.
Objectives
After completing this course, you will be able to:
- Understand the importance, principles, and fields of AI.
- Implement basic Artificial Intelligence concepts with Python.
- Apply regression and classification concepts to real-world problems.
- Perform predictive analysis using decision trees and random forests.
- Carry out clustering using the k-means and mean shift algorithms.
- Understand the fundamentals of deep learning via practical examples.
Audience
This course is for you if you Artificial Intelligence and Machine Learning Fundamentals is for software developers and data scientists who want to enrich their projects with machine learning. You do not need any prior experience in AI. However, it’s recommended that you have knowledge of high school-level mathematics and at least one programming language (preferably Python).
Prerequisites
-
Technical Requirements Hardware:
For the optimal student experience, we recommend the following hardware configuration:
- Processor: Intel Core i5 or equivalent
- Memory: 8 GB RAM
- Storage: 35 GB available space
- An internet connection
- OS: Windows 7 SP1 64-bit, Windows 8.1 64-bit or Windows 10 64-bit, Ubuntu
- Linux, or the latest version of macOS
- Browser: Google Chrome (latest version)
- Anaconda (latest version)
- IPython (latest version)
Topics
Lesson 1: Principles of Artificial Intelligence
- Fields and Applications of Artifcial Intelligence
- AI Tools and Learning Models
- The Role of Python in Artifcial Intelligence
- Python for Game AI
- Heuristics
- Pathfnding with the A* Algorithm
- Game AI with the Minmax Algorithm and Alpha-Beta Pruning
- Linear Regression with One Variable
- Linear Regression with Multiple Variables
- Polynomial and Support Vector Regression
- The Fundamentals of Classifcation
- Classifcation with Support Vector Machines
- Introduction to Decision Trees
- Random Forest Classifer
- Introduction to Clustering
- The k-means Algorithm
- Mean Shift Algorithm
- TensorFlow for Python
- Introduction to Neural Networks
- Deep Learning
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Self-Paced Training Info
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- Same in-demand topics as instructor-led public and private classes.
- Standalone learning or supplemental reinforcement.
- e-Learning content varies by course and technology.
- View the Self-Paced version of this outline and what is included in the SPVC course.
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